This review article provides a comprehensive analysis of computational methods for predicting anti-inflammatory peptides (AIPs). Inflammation is a biological response to harmful stimuli, essential for eliminating pathogens and facilitating tissue repair. However, prolonged inflammation can lead to chronic diseases. Peptide therapeutics have gained attention due to their high specificity and low toxicity. AIPs are crucial for controlling immune responses and maintaining tissue balance. Computational models have been developed to accurately predict AIPs, showing diversity in datasets, feature representation, selection, training, and evaluation methods. This study surveys existing models, evaluates their prediction results using independent tests and AUC values, and highlights challenges and future prospects in this field. The review emphasizes the importance of AIPs in various diseases and their potential applications in novel treatments for autoimmune and inflammatory conditions. Peptides, as macromolecules with fewer than 50 amino acids, can target specific molecules in the body, offering promising therapeutic opportunities. They play vital roles in human physiology, including growth factors, hormones, neurotransmitters, and anti-infectives. AIPs can reduce tissue damage and support repair mechanisms by influencing signal molecules, thus attenuating inflammation. Artificially created AIPs have shown effectiveness in treating autoimmune and inflammatory diseases by interacting with cellular membranes and regulating immune cell functions. The potential applications of AIPs extend beyond their current uses, highlighting their importance in developing more effective therapeutic strategies. The review also discusses the role of inflammation in various diseases and the need for further research to improve prediction models for AIPs.This review article provides a comprehensive analysis of computational methods for predicting anti-inflammatory peptides (AIPs). Inflammation is a biological response to harmful stimuli, essential for eliminating pathogens and facilitating tissue repair. However, prolonged inflammation can lead to chronic diseases. Peptide therapeutics have gained attention due to their high specificity and low toxicity. AIPs are crucial for controlling immune responses and maintaining tissue balance. Computational models have been developed to accurately predict AIPs, showing diversity in datasets, feature representation, selection, training, and evaluation methods. This study surveys existing models, evaluates their prediction results using independent tests and AUC values, and highlights challenges and future prospects in this field. The review emphasizes the importance of AIPs in various diseases and their potential applications in novel treatments for autoimmune and inflammatory conditions. Peptides, as macromolecules with fewer than 50 amino acids, can target specific molecules in the body, offering promising therapeutic opportunities. They play vital roles in human physiology, including growth factors, hormones, neurotransmitters, and anti-infectives. AIPs can reduce tissue damage and support repair mechanisms by influencing signal molecules, thus attenuating inflammation. Artificially created AIPs have shown effectiveness in treating autoimmune and inflammatory diseases by interacting with cellular membranes and regulating immune cell functions. The potential applications of AIPs extend beyond their current uses, highlighting their importance in developing more effective therapeutic strategies. The review also discusses the role of inflammation in various diseases and the need for further research to improve prediction models for AIPs.